Image segmentation typically involves separating object regions of an image from background regions of the image. Many different approaches for segmenting an image have been proposed, including thresholding, region growing, and watershed transform based image segmentation processes. The segmentation results of such processes may be used for a wide variety of different applications, including object extraction for object description, object detection, object recognition, and object tracking. In general, for each such vision task, the “correct” image segmentation is different. For example, a coarse skin color map may suffice as a first step for face detection, whereas for face recognition every element of the face must be segmented. As a result, many existing techniques require considerable manual intervention, with users required to tune parameters per image to obtain good segmentations.
What are needed are apparatus and methods that are capable of automatically segmenting images for different applications in ways that require minimal manual intervention.